Abstract
Global brainAGE predictions using structural MRI (i.e., deviation between neuroimaging-predicted and actual age) have shown accelerated aging in early psychosis (EP) patients, but are not informative about regional differences. Additionally, the link between brain-ageing and cognition in EP has not been addressed. Here, we used machine learning algorithms to identify cognitive subgroups of EP and then applied a deep learning algorithm to assess regional patterns of brain-ageing in each subgroup compared to healthy controls (HC).
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